Visual tracking is essential for many applications such as computer vision, human-machine interfacing, and human-human interfacing. Computer vision is especially focused in security technologies such as visual surveillance, and audio and visual technologies such as analysis, classification, and editing of recorded images. Human-human interfacing includes teleconferencing and videotelephony. Accordingly, there have been many studies undertaken on visual tracking, with a number of those specifically addressing tracking accuracy and processing efficiency. A major approach to visual tracking is now based on a particle filter. The particle filter attracts attention as a time series analysis tool for systems with non-Gaussian noise, which the well known Kalman filter cannot deal with. The CONDENSATION algorithm (Conditional Density Propagation) is well known as a technique based on a particle filter (see, for example, non-patent documents 1-3).
In the Condensation algorithm, a tracked object is defined by a contour line of an arbitrary shape comprising, for example, a B-spline curve. For example, the head of a person can be tracked by defining a Greek ohm-shaped curve using B-spline. This is because the shape of a head does not basically change in association with the person's action such as turning around or bending down so that the shape of a head can be represented only by translating, expanding, contracting, or rotating the Greek ohm-shaped curve (see, for example, patent document No. 1).
Meanwhile, remarkable progress in image processing technology has enabled processing captured images by adding virtual flair to the images, which are often seen in various scenes in our daily lives. For example, the contour of an object in an image carries weight in image processing such as replacement of the background in an image by another image or blending of images. Technologies for extracting a contour line include dynamical contour model (SNAKES) whereby a mode of a contour of an object is represented using a closed curve, and the contour of the object is estimated by deforming the closed curve so that a predefined energy function is minimized (patent document No. 2 or No. 3). Also proposed is a method of acquiring an object area by using a difference in background (patent document No. 4 or No. 5).    [Non-patent document No. 1] Contour tracking by stochastic propagation of conditional density, Michael Isard and Andrew Blake, Proc. European Conf. on Computer Vision, vol. 1, pp. 343-356, Cambridge UK (1996)    [Non-patent document No. 2] CONDENSATION-conditional density propagation for visual tracking, Michael Isard and Andrew Blake, Int. J. Computer Vision, 29, 1, 5-28 (1998)    [Non-patent document No. 3] ICondensation: Unifying low-level and high-level tracking in a stochastic framework, Michael Isard and Andrew Blake, Proc 5th European Conf. Computer Vision, 1998    [Patent document No. 1] JP 2007-328747    [Patent document No. 2] JP 9-138471    [Patent document No. 3] JP 8-329254    [Patent document No. 4] JP 3930504    [Patent document No. 5] JP 2007-34733